EJSCREEN Exploration File
Data for Eastern Shore Region
Marisa Lemma
7/20/2021
Data Source
Source: US Environmental Protection Agency EJSCREEN Tool, 2020 data (last modified 7/1/21)
- Download URL: https://gaftp.epa.gov/EJSCREEN/
- Technical Documentation: https://www.epa.gov/sites/production/files/2021-04/documents/ejscreen_technical_document.pdf
About the Data
EJSCREEN is an “environmental justice (EJ) mapping and screening tool” produced by the EPA.
Variable Descriptions
glimpse(ejscreen)## Rows: 46
## Columns: 36
## $ ID <dbl> 510010901001, 510010901002, 510010901003, 510010901004, 510…
## $ PRE1960PCT <dbl> 0.34929993, 0.45737855, 0.27187865, 0.17369309, 0.19648094,…
## $ DSLPM <dbl> 0.1902349, 0.1902349, 0.1902349, 0.1902349, 0.1788288, 0.17…
## $ CANCER <dbl> 19.86096, 19.86096, 19.86096, 19.86096, 21.57291, 21.57291,…
## $ RESP <dbl> 0.2342597, 0.2342597, 0.2342597, 0.2342597, 0.2624767, 0.26…
## $ PTRAF <dbl> NA, NA, NA, NA, 143.5144430, 4.9361403, 44.3148087, NA, 74.…
## $ PWDIS <dbl> NA, NA, NA, NA, 1.163455e-04, 9.159836e-03, 2.248991e-03, 1…
## $ PNPL <dbl> 0.01407226, 0.01383525, 0.01368040, 0.01347213, 0.01390480,…
## $ PRMP <dbl> 0.06025332, 0.06554190, 0.06277244, 0.06913822, 0.66536960,…
## $ PTSDF <dbl> 0.09912080, 0.11897784, 0.11237121, 0.13666498, 0.66536960,…
## $ OZONE <dbl> 44.12727, 44.12727, 44.12727, 44.12727, 43.34960, 43.34960,…
## $ PM25 <dbl> 6.863168, 6.863168, 6.863168, 6.863168, 7.010748, 7.010748,…
## $ P_LDPNT <dbl> 66.94467, 74.53987, 60.35342, 49.67612, 52.47047, 36.74985,…
## $ P_DSLPM <dbl> 17.49458, 17.49458, 17.49458, 17.49458, 15.48154, 15.48154,…
## $ P_CANCR <dbl> 6.943091, 6.943091, 6.943091, 6.943091, 11.166566, 11.16656…
## $ P_RESP <dbl> 5.709885, 5.709885, 5.709885, 5.709885, 9.840690, 9.840690,…
## $ P_PTRAF <dbl> NA, NA, NA, NA, 41.997946, 8.993928, 23.495505, NA, 30.5144…
## $ P_PWDIS <dbl> NA, NA, NA, NA, 54.53093, 79.14500, 70.94844, 54.04574, 67.…
## $ P_PNPL <dbl> 10.246804, 9.891942, 9.654256, 9.305683, 9.997929, 10.95281…
## $ P_PRMP <dbl> 6.697439, 7.834296, 7.200346, 8.613989, 66.828961, 29.13652…
## $ P_PTSDF <dbl> 13.633698, 16.354078, 15.479741, 18.672355, 42.786562, 37.9…
## $ P_OZONE <dbl> 63.30248, 63.30248, 63.30248, 63.30248, 54.95983, 54.95983,…
## $ P_PM25 <dbl> 12.16725, 12.16725, 12.16725, 12.16725, 13.61657, 13.61657,…
## $ T_LDPNT <chr> "0.35 = fraction pre-1960 (66%ile)", "0.46 = fraction pre-1…
## $ T_DSLPM <chr> "0.19 ug/m3 (17%ile)", "0.19 ug/m3 (17%ile)", "0.19 ug/m3 (…
## $ T_CANCR <chr> "20 lifetime risk per million (6%ile)", "20 lifetime risk p…
## $ T_RESP <chr> "0.23 (5%ile)", "0.23 (5%ile)", "0.23 (5%ile)", "0.23 (…
## $ T_PTRAF <chr> NA, NA, NA, NA, "140 daily vehicles/meters distance (41%ile…
## $ T_PWDIS <chr> NA, NA, NA, NA, "0.00012 toxicity-weighted concentration/me…
## $ T_PNPL <chr> "0.014 sites/km distance (10%ile)", "0.014 sites/km distanc…
## $ T_PRMP <chr> "0.06 facilities/km distance (6%ile)", "0.066 facilities/km…
## $ T_PTSDF <chr> "0.099 facilities/km distance (13%ile)", "0.12 facilities/k…
## $ T_OZONE <chr> "44.1 ppb (63%ile)", "44.1 ppb (63%ile)", "44.1 ppb (63%ile…
## $ T_PM25 <chr> "6.86 ug/m3 (12%ile)", "6.86 ug/m3 (12%ile)", "6.86 ug/m3 (…
## $ AREALAND <dbl> 9539038, 1940180, 2348387, 4712759, 46793460, 67772458, 732…
## $ AREAWATER <dbl> 30558659, 12485, 1575429, 5770499, 1345955, 33782726, 14175…Observations are block group estimates of key environmental indicators:
- Lead paint (
PRE1960PCT) - Particulate matter levels in the air (
DSLPMandPM25) - Air toxics cancer risk (
CANCER) - Air toxics respiratory hazard index (
RESP) - Traffic proximity (
PTRAF) - Proximity to National Priorities List sites (
PNPL) - Proximity to Risk Management Plan facilities (
PRMP) - Proximity to Treatment Storage and Disposal facilities (
PTSDF) - Ozone level in the air (
OZONE) - Major direct dischargers to water (
PWDIS)
P_ indicates percentile ranks for each variable, and T_ indicates map popup text.
meta %>%
mutate(label = paste0(varname, ": ", about)) %>%
select(label) %>%
as.list()## $label
## [1] "ID: 12-digit FIPS block group code"
## [2] "PRE1960PCT: % of housing built before 1960 -- lead paint indicator"
## [3] "DSLPM: Diesel particulate matter level in the air, measured in micrograms per cubic meter"
## [4] "CANCER: Cancer risk due to toxics in the air"
## [5] "RESP: \"Ratio of exposure concentration to health-based reference concentration\""
## [6] "PTRAF: Average number of daily vehicles at major roads divided by distance in meters"
## [7] "PWDIS: Toxicity-weighted stream concentrations divided by distance in kilometers"
## [8] "PNPL: Number of National Priorities List (NPL) sites within 5 km divided by distance in kilometers"
## [9] "PRMP: Number of Risk Management Plan (RMP) facilities within 5 km divided by distance in kilometers"
## [10] "PTSDF: Number of Treatment Storage and Disposal (TSDF) facilities within 5 km divided by distance in kilometers"
## [11] "OZONE: Summer daily average of ozone concentration in the air, in parts per billion"
## [12] "PM25: Yearly average PM2.5 level in the air, measured in micrograms per cubic meter"
## [13] "P_LDPNT: Nationwide percentile score for lead paint indicator (from 0-100)"
## [14] "P_DSLPM: Nationwide percentile score for diesel particulate matter level (from 0-100)"
## [15] "P_CANCR: Nationwide percentile score for cancer risk (from 0-100)"
## [16] "P_RESP: Nationwide percentile score for respiratory hazard index (from 0-100)"
## [17] "P_PTRAF: Nationwide percentile score for proximity to traffic (from 0-100)"
## [18] "P_PWDIS: Nationwide percentile score for major direct dischargers to water (from 0-100)"
## [19] "P_PNPL: Nationwide percentile score for proximity to NPL sites (from 0-100)"
## [20] "P_PRMP: Nationwide percentile score for proximity to RMP facilities (from 0-100)"
## [21] "P_PTSDF: Nationwide percentile score for proximity to TSDF facilities (from 0-100)"
## [22] "P_OZONE: Nationwide percentile score for ozone level (from 0-100)"
## [23] "P_PM25: Nationwide percentile score for PM2.5 level (from 0-100)"
## [24] "T_LDPNT: Map text for lead paint indicator"
## [25] "T_DSLPM: Map text for diesel particulate matter level"
## [26] "T_CANCR: Map text for cancer risk"
## [27] "T_RESP: Map text for respiratory hazard index"
## [28] "T_PTRAF: Map text for proximity to traffic"
## [29] "T_PWDIS: Map text for major direct dischargers to water"
## [30] "T_PNPL: Map text for proximity to NPL sites"
## [31] "T_PRMP: Map text for proximity to RMP facilities"
## [32] "T_PTSDF: Map text for proximity to TSDF facilities"
## [33] "T_OZONE: Map text for ozone level"
## [34] "T_PM25: Map text for PM2.5 level"
## [35] "AREALAND: Land area (in square meters)"
## [36] "AREAWATER: Water area (in square meters)"Summary Statistics
ejscreen %>% select(-c(ID, T_LDPNT:T_PM25)) %>%
select(where(~is.numeric(.x) && !is.na(.x))) %>%
as.data.frame() %>%
stargazer(., type = "text", title = "Summary Statistics", digits = 0,
summary.stat = c("mean", "sd", "min", "median", "max"))##
## Summary Statistics
## ===============================================================
## Statistic Mean St. Dev. Min Median Max
## ---------------------------------------------------------------
## PRE1960PCT 0 0 0 0 1
## DSLPM 0 0 0 0 0
## CANCER 21 1 20 21 22
## RESP 0 0 0 0 0
## PNPL 0 0 0 0 0
## PRMP 0 0 0 0 1
## PTSDF 0 0 0 0 1
## OZONE 45 1 43 45 45
## PM25 7 0 7 7 7
## P_LDPNT 58 20 11 61 86
## P_DSLPM 14 2 12 14 17
## P_CANCR 10 2 7 10 13
## P_RESP 9 2 6 9 12
## P_PNPL 10 6 0 9 26
## P_PRMP 27 21 0 22 72
## P_PTSDF 16 12 0 13 48
## P_OZONE 68 5 55 70 74
## P_PM25 13 1 12 13 14
## AREALAND 37,217,153 28,857,752 0 35,194,484 128,526,752
## AREAWATER 81,319,501 224,481,572 0 9,731,870.0 1,112,374,166
## ---------------------------------------------------------------Visual Distributions
Correlation Matrices
The following charts show the correlations between all combinations of variables. The darker the color, the more highly correlated a pair of variables are. The first correlation matrix shows correlations among the levels of each environmental indicator, and the second shows correlations among the percentiles of each indicator.
correlation <- ejscreen %>%
select(PRE1960PCT:PM25)
num_correlation <- cor(correlation, use = "complete.obs")
num_correlation <- round(num_correlation, digits = 2)
corrplot(num_correlation, type = {"upper"}, method = "shade",
shade.col = NA, tl.col = "black",
diag = F, addCoef.col = "black")
meta %>%
filter(varname %in% c("PRE1960PCT", "DSLPM", "CANCER", "RESP", "PTRAF", "PWDIS", "PNPL", "PRMP", "PTSDF", "OZONE", "PM25")) %>%
mutate(label = paste0(varname, ": ", about)) %>%
select(label) %>%
as.list()## $label
## [1] "PRE1960PCT: % of housing built before 1960 -- lead paint indicator"
## [2] "DSLPM: Diesel particulate matter level in the air, measured in micrograms per cubic meter"
## [3] "CANCER: Cancer risk due to toxics in the air"
## [4] "RESP: \"Ratio of exposure concentration to health-based reference concentration\""
## [5] "PTRAF: Average number of daily vehicles at major roads divided by distance in meters"
## [6] "PWDIS: Toxicity-weighted stream concentrations divided by distance in kilometers"
## [7] "PNPL: Number of National Priorities List (NPL) sites within 5 km divided by distance in kilometers"
## [8] "PRMP: Number of Risk Management Plan (RMP) facilities within 5 km divided by distance in kilometers"
## [9] "PTSDF: Number of Treatment Storage and Disposal (TSDF) facilities within 5 km divided by distance in kilometers"
## [10] "OZONE: Summer daily average of ozone concentration in the air, in parts per billion"
## [11] "PM25: Yearly average PM2.5 level in the air, measured in micrograms per cubic meter"correlation2 <- ejscreen %>%
select(P_LDPNT:P_PM25)
num_correlation2 <- cor(correlation2, use = "complete.obs")
num_correlation2 <- round(num_correlation2, digits = 2)
corrplot(num_correlation2, type = {"upper"}, method = "shade",
shade.col = NA, tl.col = "black",
diag = F, addCoef.col = "black")
meta %>%
filter(varname %in% c("P_LDPNT", "P_DSLPM", "P_CANCR", "P_RESP", "P_PTRAF", "P_PWDIS", "P_PNPL", "P_PRMP", "P_PTSDF", "P_OZONE", "P_PM25")) %>%
mutate(label = paste0(varname, ": ", about)) %>%
select(label) %>%
as.list()## $label
## [1] "P_LDPNT: Nationwide percentile score for lead paint indicator (from 0-100)"
## [2] "P_DSLPM: Nationwide percentile score for diesel particulate matter level (from 0-100)"
## [3] "P_CANCR: Nationwide percentile score for cancer risk (from 0-100)"
## [4] "P_RESP: Nationwide percentile score for respiratory hazard index (from 0-100)"
## [5] "P_PTRAF: Nationwide percentile score for proximity to traffic (from 0-100)"
## [6] "P_PWDIS: Nationwide percentile score for major direct dischargers to water (from 0-100)"
## [7] "P_PNPL: Nationwide percentile score for proximity to NPL sites (from 0-100)"
## [8] "P_PRMP: Nationwide percentile score for proximity to RMP facilities (from 0-100)"
## [9] "P_PTSDF: Nationwide percentile score for proximity to TSDF facilities (from 0-100)"
## [10] "P_OZONE: Nationwide percentile score for ozone level (from 0-100)"
## [11] "P_PM25: Nationwide percentile score for PM2.5 level (from 0-100)"Ozone vs. PM2.5
These scatterplots show the relationship between ozone and PM2.5, broken down by county. The first scatterplot shows the correlation between the levels of ozone and PM2.5, and the second scatterplot shows the correlation between the percentiles.
ejscreen <- ejscreen %>%
mutate(county = str_sub(ID, 3, 5))
ejscreen %>%
filter(OZONE!=0 & PM25!=0) %>%
ggplot() +
geom_point(aes(x=OZONE, y=PM25, color=county)) +
labs(x="Ozone level",
y="PM2.5 level") +
scale_color_brewer(type = "qual", labels = c("Accomack", "Northampton"))
ejscreen %>%
filter(OZONE!=0 & PM25!=0) %>%
ggplot() +
geom_point(aes(x=P_OZONE, y=P_PM25, color=county)) +
labs(x="Ozone percentile",
y="PM2.5 percentile") +
scale_color_brewer(type = "qual", labels = c("Accomack", "Northampton"))
Proximity to traffic vs. air toxics cancer risk
These scatterplots show the relationship between a block group’s proximity to traffic and air toxics cancer risk, broken down by county. The first one shows the correlation between the levels, and the second one shows the correlation between the percentiles.
ejscreen %>%
filter(PTRAF!=0 & CANCER!=0) %>%
ggplot() +
geom_point(aes(x=PTRAF, y=CANCER, color=county)) +
labs(x="Proximity to traffic",
y="Cancer risk") +
scale_color_brewer(type = "qual", labels = c("Accomack", "Northampton"))
ejscreen %>%
filter(PTRAF!=0 & CANCER!=0) %>%
ggplot() +
geom_point(aes(x=P_PTRAF, y=P_CANCR, color=county)) +
labs(x="Traffic proximity percentile",
y="Cancer risk percentile") +
scale_color_brewer(type = "qual", labels = c("Accomack", "Northampton"))
Proximity to traffic vs. diesel particulate matter level
These scatterplots show the relationship between a block group’s proximity to traffic and its diesel particulate matter level, broken down by county. The first shows the correlation between the levels, and the second shows the correlation between the percentiles.
ejscreen %>%
filter(PTRAF!=0 & DSLPM!=0) %>%
ggplot() +
geom_point(aes(x=PTRAF, y=DSLPM, color=county)) +
labs(x="Proximity to traffic",
y="Diesel particulate matter level") +
scale_color_brewer(type = "qual", labels = c("Accomack", "Northampton"))
ejscreen %>%
filter(PTRAF!=0 & DSLPM!=0) %>%
ggplot() +
geom_point(aes(x=P_PTRAF, y=P_DSLPM, color=county)) +
labs(x="Traffic proximity percentile",
y="Diesel particulate matter percentile") +
scale_color_brewer(type = "qual", labels = c("Accomack", "Northampton"))
PM2.5 vs. diesel particulate matter level
These scatterplots show the relationship between PM2.5 and diesel particulate matter, broken down by county. The first shows the correlation between the levels, and the second shows the correlation between the percentiles.
ejscreen %>%
filter(PM25!=0 & DSLPM!=0) %>%
ggplot() +
geom_point(aes(x=PM25, y=DSLPM, color=county)) +
labs(x="PM2.5 level",
y="Diesel particulate matter level") +
scale_color_brewer(type = "qual", labels = c("Accomack", "Northampton"))
ejscreen %>%
filter(PM25!=0 & DSLPM!=0) %>%
ggplot() +
geom_point(aes(x=P_PM25, y=P_DSLPM, color=county)) +
labs(x="PM2.5 percentile",
y="Diesel particulate matter percentile") +
scale_color_brewer(type = "qual", labels = c("Accomack", "Northampton"))
Spatial Distributions
Proximity to treatment storage and disposal facilities (TSDFs)
pal <- colorNumeric("Blues", reverse = TRUE, domain = easternshapes$PTSDF)
leaflet(easternshapes) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = easternshapes,
fillColor = ~pal(PTSDF),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(weight = 2, fillOpacity = 0.8, bringToFront = T),
popup = paste0("FIPS Code: ", easternshapes$GEOID, "<br>",
"Proximity to TSDF: ", easternshapes$T_PTSDF)) %>%
addLegend("bottomright", pal = pal, values = easternshapes$PTSDF,
title = "Proximity to TSDF", opacity = 0.7)Proximity to traffic
pal <- colorNumeric("Blues", reverse = TRUE, domain = easternshapes$PTRAF)
leaflet(easternshapes) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = easternshapes,
fillColor = ~pal(PTRAF),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(weight = 2, fillOpacity = 0.8, bringToFront = T),
popup = paste0("FIPS Code: ", easternshapes$GEOID, "<br>",
"Proximity to traffic: ", easternshapes$T_PTRAF)) %>%
addLegend("bottomright", pal = pal, values = easternshapes$PTRAF,
title = "Traffic Proximity", opacity = 0.7)PM2.5 Percentile
pal <- colorNumeric("Blues", reverse = TRUE, domain = easternshapes$P_PM25)
leaflet(easternshapes) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = easternshapes,
fillColor = ~pal(P_PM25),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(weight = 2, fillOpacity = 0.8, bringToFront = T),
popup = paste0("FIPS Code: ", easternshapes$GEOID, "<br>",
"PM2.5 Percentile: ", easternshapes$P_PM25)) %>%
addLegend("bottomright", pal = pal, values = easternshapes$P_PM25,
title = "PM2.5 Percentiles", opacity = 0.7)Distribution of PM2.5
pal <- colorNumeric("Blues", reverse = TRUE, domain = easternshapes$PM25)
leaflet(easternshapes) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = easternshapes,
fillColor = ~pal(PM25),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(weight = 2, fillOpacity = 0.8, bringToFront = T),
popup = paste0("FIPS Code: ", easternshapes$GEOID, "<br>",
"PM2.5 Level: ", easternshapes$T_PM25)) %>%
addLegend("bottomright", pal = pal, values = easternshapes$PM25,
title = "PM2.5 Concentrations", opacity = 0.7)Air toxics cancer risk
pal <- colorNumeric("Blues", reverse = TRUE, domain = easternshapes$CANCER)
leaflet(easternshapes) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = easternshapes,
fillColor = ~pal(CANCER),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(weight = 2, fillOpacity = 0.8, bringToFront = T),
popup = paste0("FIPS Code: ", easternshapes$GEOID, "<br>",
"Cancer Risk: ", easternshapes$T_CANCR)) %>%
addLegend("bottomright", pal = pal, values = easternshapes$CANCER,
title = "Air Toxics Cancer Risk", opacity = 0.7)Diesel Particulate Matter Level
pal <- colorNumeric("Blues", reverse = TRUE, domain = easternshapes$DSLPM)
leaflet(easternshapes) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = easternshapes,
fillColor = ~pal(DSLPM),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(weight = 2, fillOpacity = 0.8, bringToFront = T),
popup = paste0("FIPS Code: ", easternshapes$GEOID, "<br>",
"DSLPM: ", easternshapes$T_DSLPM)) %>%
addLegend("bottomright", pal = pal, values = easternshapes$DSLPM,
title = "Diesel Particulate Matter Level", opacity = 0.7)Ozone Levels
pal <- colorNumeric("Blues", reverse = TRUE, domain = easternshapes$OZONE)
leaflet(easternshapes) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = easternshapes,
fillColor = ~pal(OZONE),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(weight = 2, fillOpacity = 0.8, bringToFront = T),
popup = paste0("FIPS Code: ", easternshapes$GEOID, "<br>",
"Ozone Level: ", easternshapes$T_OZONE)) %>%
addLegend("bottomright", pal = pal, values = easternshapes$OZONE,
title = "Ozone Levels in the Air", opacity = 0.7)Important Notes
PM2.5, ozone, and NATA indicators (cancer risk, respiratory hazard index, and diesel particulate matter) are measured at the census tract level, and the same value is assigned to each block group within that tract.
Certain variables contain “NA” values for some block groups in this region. It is unclear to me why there are NAs, but my guess would be that it is due to small populations in these areas.